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Nowcasting COVID-19 Statistics Reported with Delay: A Case-Study of Sweden and the UK

Author

Listed:
  • Adam Altmejd

    (Swedish Institute for Social Research, Stockholm University, 106 91 Stockholm, Sweden
    Department of Finance, Stockholm School of Economics, 113 83 Stockholm, Sweden)

  • Joacim Rocklöv

    (Heidelberg Institute of Global Health (HIGH), Interdisciplinary Centre for Scientific Computing (IWR), Heidelberg University, 69117 Heidelberg, Germany
    Department of Public Health and Clinical Medicine, Umeå University, 901 87 Umeå, Sweden)

  • Jonas Wallin

    (Department of Statistics, Lund University, 221 07 Lund, Sweden)

Abstract

The COVID-19 pandemic has demonstrated the importance of unbiased, real-time statistics of trends in disease events in order to achieve an effective response. Because of reporting delays, real-time statistics frequently underestimate the total number of infections, hospitalizations and deaths. When studied by event date, such delays also risk creating an illusion of a downward trend. Here, we describe a statistical methodology for predicting true daily quantities and their uncertainty, estimated using historical reporting delays. The methodology takes into account the observed distribution pattern of the lag. It is derived from the “removal method”—a well-established estimation framework in the field of ecology.

Suggested Citation

  • Adam Altmejd & Joacim Rocklöv & Jonas Wallin, 2023. "Nowcasting COVID-19 Statistics Reported with Delay: A Case-Study of Sweden and the UK," IJERPH, MDPI, vol. 20(4), pages 1-14, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3040-:d:1062932
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    References listed on IDEAS

    as
    1. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    2. Jeffrey D Sachs & Salim S Abdool Karim & Lara Aknin & Joseph Allen & Kirsten Brosbol & Francesca Colombo & Gabriela Cuevas Barron & Maria Fernanda Espinosa & Vitor Gaspar & Alejandro Gaviria & Andy Ha, 2022. "The Lancet Commission on lessons for the future from the COVID-19 pandemic," DEOS Working Papers 2226, Athens University of Economics and Business.
    3. Sarah F McGough & Michael A Johansson & Marc Lipsitch & Nicolas A Menzies, 2020. "Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-20, April.
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    Keywords

    COVID-19; nowcasting; prediction;
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